Control asset comparative performance analysis system and methodology

ABSTRACT

A system and method is provided for determining the variability induced on a process output. The method includes the analysis of input variable values to determine the total variability. A series of processes may be analyzed and ranked so that a process owner may gain an understanding of how a target process performs relative to the processes of other process owners. The method includes the generation of graphical process comparisons and advice regarding asset performance. The method also includes the estimation of cost impacts due to changes in induced variability.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 60/969,102, filed Aug. 30, 2007, which is incorporated by referencein its entirety. This application is a division of U.S. Non-Provisionalapplication Ser. No. 13/195,988, filed Aug. 2, 2011, which isincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to a system and method for measurement andcomparative performance analysis of assets for production facilities.

2. Background Summary

Manufacturers make large investments in assets, e.g. personnel,instruments and equipment, field wiring, operator interfaces, automationsystems, computers and software applications, to maximize profits and toimprove safe operations, the benefits of which include better control ofproduction rates, higher quality products manufactured by theirproduction facilities with lower production risks and improved safety.

BRIEF SUMMARY OF THE INVENTION

A system and method for measurement and comparative performance analysisfor production environments is provided. In a manufacturing plant,control assets can have varying degrees of success in controlperformance depending on several factors including but not limited to:the mechanical integrity of the process equipment being controlled, theselection of the control assets employed, the mechanical integrity ofthe assets, the accuracy and reliability of the data provided by theinstruments, the design and control strategies used, the capabilities ofthe software used to express the control strategies, the skills of thepeople responsible for maintaining the assets, the tuning of theadjustable parameters in the software, and the tuning of the adjustablehardware setting of the final control element instruments. Themanufacturing facility's production capacity, quality and yield areaffected by the varying performance of the control assets.

Previously, companies have experienced a long felt and unmet need toevaluate the effectiveness of assets and to compare the performance ofthe assets to those employed by their competition in order to identifytheir competitive position, to evaluate opportunities to maximize theircurrent investments, and to evaluate the opportunity to improve theircompetitive position by making new asset investments.

Overall product quality, production rates, efficiencies, and yieldsproduced are not solely dependent on the control assets performance. Inthe context of a manufacturing facility, the quality of the rawmaterials used to produce products will also impact final productmeasurements. In addition, the consistency and smoothness of theoperation of the facility has a direct impact on the amount of variationthat is imposed on the manufacturing process. These process variationscan result in lower production, lower product quality, and lowerefficiency unless eliminated by the control assets.

Control assets are not capable of eliminating an infinite amount ofprocess variation imposed by the variability of the process, but canreduce the negative impacts to the greatest extent possible. Previously,there was no systematic and universally comparable method (1) to assesscontrol asset performance by way of measuring the effectiveness of thereduction of variation achieved by the control assets, (2) to separatethe financial gains that could be achieved by improving the processvariation, or (3) to determine the effectiveness to which the variationcan be rejected by the control assets.

The separation of the process variation impacts from the control assetscapability to reduce the impacts has important implications on the coststo improve performance. Process variation can often be reduced by low orno cost changes in operating practices and procedures, which serve toreduce the process variation if the impact of these variations can bemeasured and evaluated.

Low or no capital cost improvements in control performance can also beachieved by tuning existing control assets. Controls are often“de-tuned” to move less aggressively, in order to satisfy personnel'sdesire for slow and understandable changes. This de-tuning serves toimprove acceptance of the closed loop operation of the controlapplication mechanism, system, or device. In a manufacturing plant,operators are often empowered to put the controller in “open loop,” orotherwise defeat the action of the controller, if they are uncomfortablewith the aggressiveness or efficacy of the controller's actions.De-tuning typically results in lower performance and higher processvariability. The degree to which operators accept aggressive tuning isindividualistic. Thus, controls often have the capability to reducevariability if more aggressively tuned. In accordance with the presentinvention, improved tuning of the existing control assets can beachieved if the impact can be measured and effectively communicated.

Alternately, new, or upgrades to, control assets could be employed toincrease performance, resulting in increased capital costs. Withoutseparation of the process variation impact from the control performanceimpact, expensive investments might be made in control assets whichmight not result in the improvements targeted. For example, a newcontrol application costing over one million US dollars might beinstalled to reduce variation when simple actions to reduce processvariation and tune existing controls may have been just as effective atlittle or no capital cost.

Expensive new control application mechanisms, systems, and devices canalso fail due to unrealistic expectations of the amount of variationreduction, resulting in disappointment and potential failure. Ifrealistic expectations can be set initially, then a reasonablecombination of operational changes and control application mechanisms,systems, or devices can be designed with realistic expectations forimprovements. In accordance with the present invention, by comparing thedegree of variation reduction targeted by the proposed new controls tothe degree of variation reduction achieved by the leaders in theindustry, a realistic expectation of improvement can be set. This canonly be accomplished if the variation reduction due to controls can beseparated from the degree of variation imposed by the inputs to theprocess.

Similarly, new control application mechanisms, systems, or devices havebeen installed because management felt that advanced controls mustsurely be required for the type of process being controlled. Management,in the absence of the objective measurements of the variability levelsand reduction that is achievable through the use of the method andsystem according to the present invention, often thinks in terms of an“automation gap.” The shorthand for this automation could be describedas follows: “The competition has control assets employed that we do not,therefore we need them, too.” When the expensive control investment isinstalled, management is disappointed to find that little improvement isachieved. Within a short time the control asset is abandoned, and theproject is considered a failure. If an objective measurement of theprocess variability were available initially, management would havelearned that the present product variation compares well with thecompetition despite having only simple controls. In a manufacturingplant, use of the present invention would have revealed that this isbecause the raw materials, operating practices, and process variation issmall, resulting in little variation to be rejected, and therefore noneed for expensive advanced controls.

The converse can also occur, where management has had little successwith control applications, and as a result they fail to make criticalcontrol asset investments. The competition can gain a significantadvantage in this case.

The separate identification, comparison, and assessment of economicopportunity allows for reduction of variation in performance. Thefollowing description is given in the context of the oil refiningindustry. However, the method and system are universally applicable witheasy extension of the metrics and methodology into any productionenvironment, including but not limited to: power generation andtransmission; pharmaceutical manufacturing; food and beveragemanufacturing; the pulp and paper industry; petrochemical manufacturing;organic and inorganic chemical manufacturing; the polymers and plasticsindustry; the operation of industrial, power and marine boilers;automotive manufacturing; internal combustion engine control; medicalequipment manufacturing; metals and mining industry; packaging; mail andpackage processing; construction; project development; andtransportation; as well as, a host of other industry and businessapplications.

According to one (or an) embodiment, a system and method is disclosedfor comparative operational and process control performance analysis ofindustrial process units using unique algorithms, graphical presentationmethods and economic gap calculations all based on reduction of processvariability. While the process and manufacturing facility in severalembodiments pertain to the hydrocarbon and chemical process industries,the present invention applies to control assets generally and includebut are not limited to sales, marketing, transportation, projectdevelopment, and construction applications as well.

Embodiments of a method relate to the various refining process unittypes, including, but not limited to, crude distillation, vacuumdistillation, catalytic reforming, catalytic cracking, hydrocracking,hydrotreating, and delayed coking Direct extensions of the method inrefining alone include: visbreaking, thermal cracking, hydrogengeneration, hydrogen purification, MTBE production, Alkylation,Isomerization, desulfurization, sulfur recovery, tail gas recovery,sulfuric acid generation, asphalt and bitumen production, cokecalcinators, desalination, CO2 liquification, cumene, cyclohexane,hydrodealkylation, toluene, xylene, paraxylene, ethybenzene,deisopenanizer, deisohexanizer, dehaptanizer, alkyate/reformatesplitter, solvent deasphalting, aromatic solvent extraction, extractivedistillation, calicination, and propane/propylene splitting among otherrefining processes.

One embodiment is a computer-implemented method for determining theamount of induced variability of variables in a process comprising thesteps of: collecting a plurality of datasets of input variable valuesand output variable values; calculating standard deviations for each ofthe datasets of input variable values and output variable values; anddetermining induced variability of each of the datasets of outputvariable values.

Another embodiment is a computer-implemented method of automating thepresentation of advice on process control asset performance comprisingthe steps of: collecting a plurality of datasets of input variablevalues and output variable values; calculating standard deviations foreach of the datasets of input variable values and output variablevalues; calculating induced variability of each of the datasets ofoutput variable values; calculating output variability of each of thedatasets of output variable values; calculating a reduction invariability for at least two processes; and generating advice based onthe calculated induced variability, calculated output variability, andreduction in variability of a target process.

Another embodiment is a computer-implemented method of automating thepresentation of advice on control asset performance comprising the stepsof: selecting a set of input variables; selecting a set of outputvariables, wherein the variability of the selected output variablevalues is affected by the variability of the selected input variablevalues; collecting a plurality of datasets of input variable values andoutput variable values for the input variables and the output variables;processing the input variable values and the output variable values toremove outliers; wherein the processing comprises: removing data errors;calculating standard deviations for each of the processed datasets ofinput variable values and output variable values; estimating combinedvariability of each of the processed datasets of input variable values;calculating induced variability of each of the processed datasets ofoutput variable values; calculating output variability of each of theprocessed datasets of output variable values; calculating variabilityratio for each of the processed datasets of output variable values;calculating the overall induced variability for at least four processes;calculating the overall output variability for at least four processes;calculating the overall reduction in variability for at least fourprocesses; rank ordering the processes by overall induced variabilityand overall output variability; separating the processes into at leastone category based on at least one overall variability, wherein thecategories comprise: quartiles based on overall induced variability, andquartiles based on overall output variability; constructing a graph ofthe processes units with at least one category displayed, wherein thegraph comprises: lines dividing the processes into quartiles by overallinduced variability, lines dividing the processes into quartiles byoverall output variability, and radial lines extending from the origindividing the processes into quartiles by overall reduction invariability; displaying the overall induced variability and overalloutput variability of a target process on the graph; and generatingadvice based on the category of the target process.

Yet another embodiment is a system comprising: a server, comprising: aprocessor, and a storage subsystem; a database stored by the storagesubsystem comprising: input and output data; a computer program storedby the storage subsystem, when executed causing the processor to:collect a plurality of datasets of input variable values and outputvariable values; calculate standard deviations for each of the datasetsof input variable values and output variable values; and determineinduced variability of each of the datasets of output variable values.

Another embodiment is a system comprising: a server, comprising: aprocessor, and a storage subsystem; a database stored by the storagesubsystem comprising: input and output data; a computer program storedby the storage subsystem, when executed causing the processor to:collect a plurality of datasets of input variable values and outputvariable values; calculate standard deviations for each of the datasetsof input variable values and output variable values; calculate inducedvariability of each of the datasets of output variable values; calculateoutput variability of each of the datasets of output variable values;calculate a reduction in variability for at least two processes; andgenerate advice based on the calculated induced variability, calculatedoutput variability, and reduction in variability of a target process.

Another embodiment is a system comprising: a server, comprising: aprocessor, and a storage subsystem; a database stored by the storagesubsystem comprising: input and output data; a computer program storedby the storage subsystem, when executed causing the processor to: selecta set of input variables; select a set of output variables, wherein thevariability of the selected output variable values is affected by thevariability of the selected input variable values; collect a pluralityof datasets of input variable values and output variable values for theinput variables and the output variables; process the input variablevalues and the output variable values to remove outliers, wherein theprocessing comprises: removing data errors; calculate standarddeviations for each of the processed datasets of input variable valuesand output variable values; estimate the combined variability of each ofthe processed datasets of input variable values using the calculatedstandard deviations; calculate the induced variability of each of theprocessed datasets of output variable values using the calculatedstandard deviations; calculate the output variability of each of theprocessed datasets of output variable values using the calculatedstandard deviations; calculate the variability ratio for each of theprocessed datasets of output variable values using the induced andoutput variabilities; calculate the overall induced variability for atleast four processes using the induced variability of the processeddatasets; calculate the overall output variability for at least fourprocesses using the output variability of the processed datasets;calculate the overall reduction in variability for at least fourprocesses using the induced and output variabilities; rank order theprocesses by overall induced variability and overall output variability;separate the processes into at least one category based on at least oneoverall variability, wherein the categories comprise: quartiles based onoverall induced variability, and quartiles based on overall outputvariability; constructing a graph of the processes with at least onecategory displayed, wherein the graph comprises: lines dividing theprocesses into quartiles by overall induced variability, lines dividingthe processes into quartiles by overall output variability, and radiallines extending from the origin dividing the processes into quartiles byoverall reduction in variability; display the overall inducedvariability and overall output variability of a target process on thegraph; and generate advice based on the category of the target process.

Another embodiment is a computer-implemented method for estimatingenergy savings for a process comprising the steps of: collecting aplurality of datasets of input variable values; calculating the standarddeviations for each of the processed datasets of the input variablevalues; collecting a set of standard deviation benchmarks correspondingto at least one input variable; calculating a difference between thestandard deviation of at least one input and at least one correspondingstandard deviation benchmark; and estimating the savings related to thedifference.

Another embodiment is a system comprising: a server, comprising: aprocessor, and a storage subsystem; a database stored by the storagesubsystem comprising: input and output data; a computer program storedby the storage subsystem, when executed causing the processor to:collect a plurality of datasets of input variable values; calculate thestandard deviations for each of the processed datasets of the inputvariable values; collect a set of standard deviation benchmarkscorresponding to at least one input variable; calculate the differencebetween the standard deviation of at least one input and at least onecorresponding standard deviation benchmark; and estimate the savingsrelated to the difference.

BRIEF DESCRIPTION OF THE DRAWINGS

These and further features will be apparent with reference to thefollowing description and drawings, wherein:

FIG. 1 is a flow chart illustrating one embodiment of a method tocalculate production unit variability metrics.

FIG. 2 is a diagram of one embodiment of an induced variability gainmagnitude matrix construction for a crude unit. Similar inducedvariability gain magnitude matrix constructions have been reduced topractice for all major refinery process units. Similar constructions arecontemplated for all major process units in all continuous anddiscontinuous process operations.

FIG. 3 is a flow chart of an embodiment of a method to calculate thegain magnitude values in any induced variability gain matrix.

FIG. 4 is a diagram illustrating an embodiment of a method to obtaininitial estimates of variability gains by examination of the boilingpoint curves of various refinery crude feeds.

FIG. 5 is a diagram showing an embodiment of a method of analysis ofprocess improvements by use of a variability metrics.

FIG. 6 is a diagram illustrating exemplary economic yield benefits thatcan be estimated through the use of the novel metrics of the disclosedembodiments.

FIG. 7 is a diagram illustrating exemplary economic energy benefits thatcan be estimated through the use of the novel metrics of the disclosedembodiments.

FIG. 8 is a diagram illustrating an embodiment for a crude unit of theunique Variability Graph which utilizes the novel metrics of thedisclosed embodiments to easily visualize and diagnose the overallperformance of crude units. Similar Variability Graphs have been reducedto practice for all major refinery process units. Similar constructionsare contemplated for all major process units in all continuous anddiscontinuous process operations.

FIG. 9 is a diagram illustrating an embodiment of a system, whichincludes the hardware and software engines that implement theembodiment.

FIG. 10 is a diagram illustrating a vector representing totalvariability on the Variability Graph.

BRIEF DESCRIPTION OF THE TABLES

These and further features will be apparent with reference to thefollowing description and tables, wherein:

TABLE 100 shows exemplary industry process input data parameterscollected to support creation of the novel metrics of the disclosedembodiments.

TABLE 200 shows exemplary industry process output data parameterscollected to support creation of the novel metrics of the disclosedembodiments.

TABLE 300 shows exemplary industry process data observation datasets ofinputs and outputs output data collected to support creation of thenovel metrics of the disclosed embodiments.

TABLE 400 shows exemplary induced variability gain magnitude matrixinputs and outputs for various refinery process unit types.

TABLE 500 shows exemplary automated advice that can be delivered basedon the unified overall metric Vo-Vi-Vrr.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Unit is broadly defined as a distinct entity within a larger group, suchas operating entities within a facility or business setting. Examples ofunits include electric power generators, chemical reactor vessels,pharmaceutical production lines, and package delivery systems.

One embodiment of the method, shown in FIG. 1, involves the use of adatabase that contains unit level process operating data for generatinga comparison. The process parameters for which data is collected areidentified in Table 100 and Table 200.

In step 100, historical process data are extracted for the target unitas defined in Table 100 and Table 200. The data are gathered for amultiplicity of data set observations of real-time uncompressedoperational data from the target process (three or more data sets arepreferred, but only one is needed). In a preferred embodiment, a minimumof three data sets are collected, each covering time frames defined inTable 300. The time frames for data collection can vary from those shownin Table 300. Data quantity can be as low as one single complete set ofinputs and outputs.

A multiplicity of data sets is collected during “Normal State”operations, defined as a period of time in which the unit is operatingnormally without large process disturbances. One embodiment uses threedata sets when the data are manually collected. For applications inwhich the data are collected automatically, any number of observationscan be collected up to and including continuous data collection.

For crude and vacuum refining units, a second multiplicity of data setsis collected during crude switch operations, defined as that period oftime in which the crude oil charge is being changed from one crudesource to another, accompanied by a change in density and compositionmeasured in API (a standard measurement of crude density) or specificgravity. These data are handled in a separate metric for crude switchperformance. Note that crude switch observations can be collected forother highly affected units in a refinery, such as gas processingplants, desalters, etc. Crude and vacuum refining units have beenreduced to practice.

For delayed coking units, a second multiplicity of data sets iscollected during drum switch operations, defined as that period of timein which the coking drum, which feeds the main fractionator is beingswitched from one drum to another. These data are handled in a separatemetric for drum switch performance.

In step 200, the data are examined and preprocessed to assure the inputinformation is valid. This step includes analysis of the values toassure the values are reasonable, the values are of the right order ofmagnitude, and the raw process data do not contain instrumentation ordata recording abnormalities such as “spikes.” Spikes are events inwhich the data for one observation show an inordinately large or smallreading and immediately return to a reasonable range. If the abnormalityindicates a change that is physically impossible for an actual operatingunit to have actually experienced, then the spike data reading iseliminated from the dataset. If the data values in general are notreasonable values, then the operating unit which supplied the data iscontacted to assure that the correct process parameters were used.Preprocessing can be done by automated checks, or can be done manually.In either case, an individual with industry experience is generally usedto assure the reasonableness of the data either by personal review ofthe data, or use of automated logic created by the individual withindustry experience.

Not all inputs are measured by the industry. Some inputs might bederived or inferred from data that is normally recorded. These readingsare called inferred inputs. In step 300, inferred input values arecalculated. Some of these inferred values are industry standardcalculations such as liquid hourly space velocity (LHSV) (calculatedfrom reactor dimensions and process flow rates) and catalyst loadings(the density of the catalyst loaded into the reaction vessel ascollected from the unit log data from the operations personnel). Otherparameters such as API could be measured online but typically are notmeasured. Another embodiment is used to infer API, which is describedbelow.

In step 400, pseudo set points of the input and output data observationsare established and added to the data set. Typically the industry doesnot maintain a long term record of set points used. Operating unitstypically record the actual process values, but not the set points. Forindustries that maintain a history of the set points, set points arepreferred for use. However, if the set points are not recorded then theyare estimated. There are several methods that can be used to estimateset points. A few of those are given below:

-   -   1. Use actual recorded set points if they exist.    -   2. Use controller statistics that are recorded by the control        application or external software applications.    -   3. Use the average value of the data during the observation        period as the estimate of the set point. This typically        introduces only small errors in that set points should not be        changed minute to minute. The observations are collected during        “normal state” in which few major process changes are being        introduced.    -   4. Use the running average of the data as the set point. This        poses some problems for the dynamics.    -   5. Use a running average to detect set point changes, and then        divide the observation into time segments with different set        points. For each segment use the average of all data in that        segment as the set point estimate.    -   6. Visually scan the data and assign set points manually.        This list of methods is illustrative and exemplary only. To        assure equal treatment among all participants, since few have        the set point information, method 3 above is preferred. However,        when set point information is more common in a field or process,        method 1 above would be preferred.

In step 500, the standard deviations of the input and output datadeviation from the pseudo set points for each variable in eachobservation data set are calculated.

In step 600, an estimate of the combined variability across themultiplicity of observations is calculated. This is done by combiningthe standard deviations from the multiplicity of observations into oneestimate of input and output standard deviations to yield σX_((k)) andVo(i). This combination may be accomplished by several methods. Themethods below are illustrative and exemplary only.

1. δX _(k)=Sqrt(((σX _(k1))²+(σX _(k2))²+ . . . +(σX _(kn))²))/n)  (I-1)

Where σX_(k)=the standard deviation of input X_(k).

σX_(kn)=the standard deviation of input X_(k) for observation period n.

n=the total number of observation periods 1 . . . n

-   -   Note that when comparing similar unit operations, equation I-1        is preferred. For comparing operations that are dissimilar and        might have values of X that are an order of magnitude different        between various operating units in the population, then the        coefficient of variation of the input parameter σX_(k):        [σX_(k)/Average Xk] should be used.

2. Vo(i)=Sqrt((σY _(k1))²+(σY _(k2))²+ . . . +(σY _(kn))²)/n)  (I-2)

Where Vo(i)=the “Output variability”=Standard deviation of outputvariable

Yi. Which equals σY_(i).

σY_(in)=the standard deviation of output Y, for observation period n.

-   -   Note that when comparing similar unit operations, equations 1-3        is preferred. For comparing operations that are dissimilar and        might have values of Y that are an order of magnitude different,        then the coefficient of variation of the output parameter Y_(i):        [σY_(i)/Average Y_(i)] should be used.

In step 700, the Induced Variability Vi(i) of each Output Variable i iscalculated. This is done using a novel Gain Matrix which estimates thevariability of product measurements from the standard deviation of theinput variables σX_(k). An example gain matrix for a crude unit is givenin FIG. 2. A unique gain matrix can be developed for each unit type.Only the crude unit gains are given as an example. An example of thegains used in the gain matrix for the example crude unit in FIG. 2 isgiven in Table 400.

The methods to develop gains according one embodiment are describedherein. The use of a gain magnitude matrix, which estimates productvariations from inducing parameter variations, is a new and novelapproach. It is also convenient that the gains used are very similar tothe gain values common in linear control applications, where themagnitude is taken of each gain for the purpose of estimating outputvariability, which is always non-negative. It is important to note that,unlike gain matrix applications in practice today for controlapplications (superposition of linear systems which adds thegain-multiplied contributions), the individual contributions from thegain magnitude calculations are not summed directly. Instead, accordingan embodiment, the individual contributions are squared and summedappropriately taking into account any correlation that may exist betweeninputs. The square root of the sum is then taken. This approach may bereferred to as “the weighted variance approach.”Vi_(i) is defined as the induced variability standard deviation ofproduct output “i” of interest. It is an estimate of the amount ofvariability that is being caused by the variability of selected inputsto the process unit and:

$\begin{matrix}{{Vi}_{i} = \lbrack {{{\sum\limits_{k = 1}^{n}{G_{o_{i} - x_{k}}^{2}*\sigma_{X_{k}}^{2}}} + {2{\sum\limits_{l = 1}^{n}{\sum\limits_{j < l}^{n}{G_{o_{i} - x_{l}}G_{o_{i} - x_{i}}*\sigma_{X_{l}}}}}}},_{X_{j}}} \rbrack} & ( {I\text{-}5} )\end{matrix}$

where:

G_(o) _(i) _(-x) _(k) =gain magnitude of the unit product output iinterest to the standard deviation of input inducing parameter X_(k).

σ² _(X) _(k) =variance of input X_(k)

σ_(X) ₁ _(,X) _(j) =covariance between inputs X₁ and X_(j)

If the inputs, X₁ and X_(j), are independent and subsequentlyuncorrelated observation, then:

${Vi}_{i} = \lbrack {\sum\limits_{k = 1}^{n}{G_{o_{i} - x_{k}}^{2}*\sigma_{X_{k}}^{2}}} \rbrack^{1/2}$

-   -   (I-5A)        As an illustration, Vi could be an estimate of the amount of        variability that is induced upon an output product property of        interest by the variability of the key process inputs.

In step 800, the dimensionless Variability Ratio Vr(o) and VariabilityReduction Ratio Vrr(o) of each output variable of interest iscalculated.

Vr _(i)=(Vo _(i) /Vi _(i))  (I-6)

Vrr _(i)=1−Vr _(i)  (I-7)

Where Vr_(i)=Variability Ratio of output product property of interest i.

-   -   Vrr_(i)=Variability Reduction Ratio of output product property        of interest i.        Note that Vr and Vrr are dimensionless numbers as all units        cancel out in the division. Dimensionless numbers have special        qualities for benchmarking as dimensionless measurements of        units of any capacity or size can be directly compared.        These two novel dimensionless parameters have specific meanings.        Vr is the fraction of the induced variability that remains in        the product. Vrr is the fraction of the induced variability that        has been removed by the unit controls. The preferred method is        to use Vrr as higher values relate to better control asset        performance. However, all calculations can be performed using Vr        alone, since Vr introduces no artificial constant and therefore        retains its dimensionless nature throughout the analysis. The        constant can interfere with some uses of the measure, however,        Despite this limitation, Vrr is the preferred metric for        communication to management, since it does not require the        audience to think in reverse terms.        The estimation of Vi and Vrr allows the separate analysis and        management of control action from process induced variability on        a stream-by-stream, property-by-property basis regardless of the        size of the units being compared.

In step 900, the overall unit output variability performance metric iscalculated. Although the product stream by stream and attribute byattribute metrics are very useful for diagnosis of methods to improveunit operations, management has need of an overall performance metric tohelp understand and compare the overall unit performance to competition.This is accomplished with the overall Vo metric and Vi metrics.

Vo=(Vo ₁ *f ₁ +Vo ₂ *f ₂ + . . . +Vo ₁ *f ₁)  (I-8)

Vi=(Vi ₁ *f ₁ +Vi ₂ *f ₂ + . . . +Vi _(i) *f _(i))  (I-9)

Where Vo=overall unit output product variability achievement.

-   -   Vi=overall unit calculated induced variability    -   Vo_(i)=average standard deviation of the measured output        variability observations on stream i    -   Vi_(i)=average standard deviation of the calculated output        variability imposed by process variation observations on stream        i    -   f_(i)=fraction of the product stream to the agglomerated total        production of interest. This can be mass fraction or volume        fractions of the total production of interest. The preferred        embodiment is the volume fraction as volumes are directly        measured but mass requires conversion of the measured values        using an approximated density that introduces errors.        Vo is the main metric for comparing units overall performance.        Vi is the main metric to compare the amount of variability        induced by process operations.        Another embodiment incorporates the importance factors by        product variable based on economics or other criteria. This is a        simple extension of the weights used.        Another embodiment uses the square root of the sum of the        squares approach combined with the weighted average as given in        the equations below:

Vo=(Vo ₁ ² *f ₁ +Vo ₂ ² *f ₂ + . . . +Vo ₁ ² *f)^(0.5)  (I-8A)

Vi=(Vi ₁ ² f ₁ +Vi ₂ ² *f ₂ + . . . +Vi _(i) ² *f _(i))^(0.5)  (I-9A)

Of course equation I-8A and I-9A honor the fact that the Vo and Vi arestandard deviations.

In step 1000, the overall unit variability ratio Vr and variabilityreduction ratio Vrr are calculated. Although the product stream bystream and attribute by attribute metrics are very useful for diagnosisof methods to improve unit operations, management has need of an overallcontrol performance metric to help understand and compare the overallunit control performance of the process unit to competition. This isaccomplished with the overall Vr and Vrr metrics.

Vr=(Vr ₁ *f ₁ +Vr ₂ *f ₂ + . . . +Vr _(i) *f _(i))  (I-10)

Vrr=1−Vr  (I-11)

-   -   Where Vr=overall variability ratio, an estimate of the fraction        of induced variability that remains in the product.    -   Vrr=overall variability reduction ratio, an estimate of the        fraction of the induced variability that has been removed from        the product by the unit controls.    -   f_(i)=fraction of the product stream i to the agglomerated total        production of interest.        Vrr is the preferred embodiment of the main metric for comparing        units overall control performance. As stated previously, Vr can        alternately be used for the same purpose, but must be understood        to be the inverse of the efficacy of the controls. Alternate        embodiments include the incorporation of importance factors by        product parameter based on economics or other criteria.        An alternate embodiment of equation I-10 is to use the square        root of the sum of the squares approach given in the equations        below:

Vr=(Vr ₁ ² *Mf ₁ +Vr ₂ ² *Mf ₂ + . . . +Vr _(i) ² *Mf_(i))^(0.5)  (I-10A)

In FIG. 3, the process for development of the key induced variabilitygains is described. The process involves gathering multiple sources ofinformation to establish the order of magnitude of the gains, developinga trial gain set, and then tuning the trial gain by testing thecalculated Vi against industry data. A final gain set is thenestablished, which is used for all study participants.

In step 2100, participation from a significant portion of the targetindustry is sought to gather the operational data that will be requiredto obtain the gains. Step 2200, which is impractical in continuous,large production processes but may be effective in discretemanufacturing, is the step of requesting that industry obtain a trainingsignal set of data for development of the Vi gains directly. In step2100, industry is asked to put all present controllers in open loop andtake no operator actions to reject disturbances for a period of time tocollect the data needed to directly determine the actual gains betweeninput disturbances and output production. Various levels of deliberatelyintroduced input disturbance might also be required. The data collectedfrom such experiments creates a measured true collected Vi signal totrain a model against. This creates a solid training signal. Step 2200would be very expensive for industry since it could produce low qualityproduction and might be unsafe to operate in the requested manner. Forthese reasons, step 2200 is not the preferred method for continuous,large production processes and may be skipped in those circumstances.

When step 2200 is impractical, it must be realized that no actualtraining signal exists to allow the Vi gains to be directly calculated.Therefore, the Vi gains must be estimated or inferred. This is done bygathering multiple sources of information from which to construct anestimate of the order of magnitude of the gains, and then testing thegains by calculating induced variability and checking the reasonablenessof the results.

In step 2300, a more reasonable approach is taken. Participation from asignificant portion of the target industry is sought to gather normaloperating data with the unit controllers in action. For the refiningindustry units, these data are defined in Table 300. The parameters tobe captured are given in Table 100 and Table 200. The gains to bedeveloped between the Inputs in Table 100 and the Outputs in Table 200for one embodiment are disclosed. Participants are asked to gather thedata and submit it for assembling an industry wide testing data set.When a reasonable result with one set of gains that produces reasonableresults for all participants in the industry training set is obtained,it will be the gain set employed.

In step 2300, participating refineries' crude slates are examined and arepresentative sampling of, for example, three to five crudes areselected for development of initial gain magnitudes. The initial gainmagnitudes are calculated from examination of the boiling point curvesof the representative crudes as shown in FIG. 4.

In step 2400, the literature is searched for reported gains from theinputs to the outputs used in actual installed industry controllers.These gains are most often obtained from step tests. Since the inducedvariability gains should be very similar to the controller processcontrol gains, the magnitudes of these process gains can be used as oneestimate of the gain magnitudes for the induced variability gains inthis analysis.

In step 2500, personal expert experience of operators and operationspersonnel is consulted to develop estimates of gain magnitudes. In suchinterviews the expert may be asked questions such as the following: “Ifyou were to increase the crude feed rate by 5,000 bpd and you did notincrease the Naphtha draw rate, how much do you think the Naphtha drawtemperature would rise?” These anecdotal responses are tabulated todetermine the approximate magnitude of the gain. The above question isan example only of the process of interviewing the expert.

In step 2600, all of the various sources of gain magnitudes from steps2100 through 2500, including those from other sources are examined todevelop an initial starting trial set of gains for testing against therepresentative industry process data.

In step 2700, the initial trial gains are tuned by successive testingand modification against the entire data set of collected representativeindustry process data created in step 2200. In this process, outlierresults for the estimate of Vi, Vr, and Vrr are examined to determinewhich input is most responsible for the error. These are adjusted withinthe reasonable bounds of the gains established in step 2600.

Once step 2700 has been repeated until the developer is satisfied thatthe best possible gains has been established, then, in step 2800, asingle set of gains is established as the analysis gain set, and thisset is applied to all participating process units. This is the preferredmethod to provide reasonable and comparable results to all industryparticipants. An alternate embodiment is to calculate a unique gain setfor each and every participating process unit, or unique gains for anyselected subset of process units.

Development of Inferred Values

Inferred values provide input values that are key concepts that are nottypically measured directly by instruments in the industry, but can becalculated from measurement that are recorded. These can be wellestablished first principle concepts, laws of physics, well establishedengineering design and analysis parameters, or novel or new concepts orcalculations that prove useful in estimation of variability of theoutput products.

By way of example of inferred values, examine Table 100, whichidentifies several inferred values. Table 100 serves as an example onlyof the use of inferred values that will be applied in other unitoperations or other industries in addition to refining.

For reformers and hydrotreaters, the well established principle ofreactor liquid hourly space velocity (LHSV) is an inferred input. Thecalculation of LHSV is well established in the industry and need not beexplained here. It is calculated from reactor dimensions and catalystloading and reactor feed rates which are measured and recorded.

For hydrocrackers and hydrotreaters, the Weighted Average BedTemperature (WABT) is an inferred input, and the calculation of WABT iswell established in the industry. Often WABT is recorded directly fromcalculations done in the distributed control system or reactortemperature controllers; however, if the WABT is not directly available,then the WABT can be calculated from the individual reactor bedtemperatures that are recorded.

For Reformers, the Weighted Average Inlet Temperature (WAIT) of thereactors is an inferred input, and the calculation is well establishedin the industry. Often WAIT is recorded directly from calculations donein the distributed control system or reactor temperature controllers;however, if the WAIT is not directly available, then the WAIT can becalculated from the individual reactor inlet temperatures which arerecorded.

For Crude and Vacuum Units, the API or density of the unit feed can bemeasured on-line but seldom is measured on-line in industry practice.The API is a rough measurement of the composition of the unit feed, andtherefore is an important input affecting the product variation andtherefore should be inferred if not directly measured.

The basic concept for the invention of the API standard deviationinferred value is to use the flow and temperature readings of the columnitself as data from a large on-line analyzer. Each column side draw hasa known product class, a typical draw tray temperature under atmosphericcolumn pressure, and a known API range. As the volume fractions of thesedraws change, and the tray temperatures change, there is an impliedchange in the crude feed composition to the unit that was required toproduce these changes in distillation products.

There are three complications that make it impractical to develop thestandard deviation of API directly from the above standard industryknowledge: 1) the overhead and base flows are not considered, only theside draw flows are given, thus the mass balance to the crude feed isincomplete; 2) we are predicting the standard deviation of variation inAPI, not the API and covariance can occur; and 3) the action of the sidedraw product controllers is to manipulate the volume percent of thedraws to maintain target properties, and thus the controllers themselvescontribute to the variation.

These complications require then a empirical correlation rather than astraight forward calculation based on first principles knowledge. Thesecorrelations were developed by a combination of first principlesknowledge and regression against industry data on scores of atmosphericand vacuum units. The results have proved to be robust.

First we will describe the crude unit crude feed API standard deviationinferred value, then describe the vacuum unit atmospheric tower bottomsfeed API standard deviation inferred value. The crude unit feed APIvariation is inferred from the standard deviations of the draw traytemperatures and flows of the column side streams as given in theequations below.

σV _(API(1)) =f ₍₁₎(3.312E-06σX ² _(temp(1))+0.06644σX _(temp(1)))+(σX_(temp(1)) *σX _(flow(1)))  (II-1)

σV _(API)=(σV _(API(1)) +σV _(API(2)) ,+ . . . +σV _(API(1)))  (II-2)

Where σV_(API)=The inferred standard deviation of crude feed API.

σV_(API(1))=The inferred contribution to the standard deviation of thecrude feed due to the standard deviation of the API of side stream (1)product.

σX_(temp(1))=The standard deviation of the draw tray temperature of sidestream (1) product.

σX_(flow(1)))=The standard deviation of the draw flow of side stream (1)product.

f₍₁₎=The fraction of side stream product (1) of the sum of all sidestream products.

Note that the sum does not include overhead gas or atmospheric towerbottoms flow.

The standard deviation of the API of the atmospheric tower bottoms feedto a vacuum unit is inferred from the standard deviations of the drawtray temperatures and flows of the vacuum column side streams as givenin the equation below.

σV _(API(1)) =f ₍₁₎(0.00002σX ² _(temp(1))+0.0427σX _(temp(1)))+(σX_(temp(1)) *σX _(flow(1)))  (II-3)

V _(API) =ΣσV _(API(1))  (II-4)

Where σV_(API)=The inferred contribution to the standard deviation ofthe crude feed due to the standard deviation of the API of side stream(1) product.

σV_(API(1))=The inferred standard deviation of API of side stream (1)product.

σX_(temp(1))=The standard deviation of the draw tray temperature of sidestream (1) product.

σX_(flow(1)))=The standard deviation of the draw flow of side stream (1)product.

f₍₁₎=The fraction of side stream product (1) of the sum of all sidestream products.

Note that the sum does not include overhead gas or vacuum tower bottomsflow.

Although the preceding examples are for specific inferred inputs forspecific units in refining, they are illustrative and exemplary, andadditional inferred calculations may be used for input values.

Calculation of Performance Metrics and Gaps

The calculated standard deviations of all input, output, and variablesfrom step 500 in FIG. 1 are gathered from all industry participatingunits. In addition, the overall performance parameters, Vo, Vi, Vr, andVrr are gathered, along with the individual stream performanceparameters Vo_(i), Vi_(i), and Vrr_(i). All of these are arranged inascending order and divided into quartiles with Quartile 1 having thelowest variation and therefore the best performance. The average of allvalues in Quartile 1 is calculated as the base line for comparison. Aperformance gap is calculated for each input and output variable as thedifference between the individual standard deviation and the Quartile 1average. The use of the Quartile 1 average is the preferred embodiment,however the difference to the combined Quartile 1 and 2 average (tophalf average) and the difference to the study average (average of allvalues) can also be calculated and reported. While quartiles are used inthis embodiment, and are common in some industries, the overall andindividual performance parameters may be separated into any number ofdivisions.

Individual Input Variability Metrics

Q1σX _(k)=Average σX _(k) of lowest 25% of collected σX _(k)  (III-1)

Q2σX _(k)=Average σX _(k) of 2^(nd) lowest 25% of the collected σX_(k)  (III-2)

Q3σX _(k)=Average σX _(k) of 2^(nd) highest 25% of the collected σX_(k)  (III-3)

Q4σX _(k)=Average σX _(k) of the highest 25% of the collected σX_(k)  (III-4)

Top3σX _(k)=Average σX _(k) of the lowest three collectedσX_(k)  (III-5)

TopHalfσX _(k)=Average σX _(k) of lowest 50% of the collected σX_(k)  (III-6)

AverageσX _(k)=AverageσX _(k) of all collected σX _(k)  (III-7)

Individual Input Variability Gaps

GapσX _(k) =σX _(k)−Selected Variability Metric from (III-1 toIII-7).  (II-8)

-   -   The preferred embodiment of GapσX_(k) is to use the Q16 X_(k),        for overall gap, and to use the others to create intermediate        gap closure goals.

Individual Output Variability Metrics

Q1Vo _(i)=Average Vo _(i) of lowest 25% of collected Vo _(i)  (III-9)

Q2Vo _(i)=Average Vo _(i) of 2^(nd) lowest 25% of the collected Vo_(i)  (III-10)

Q3Vo _(i)=Average Vo _(i) of 2^(nd) highest 25% of the collected Vo_(i)  (III-11)

Q4Vo _(i)=Average Vo _(i) of the highest 25% of the collected Vo_(i)  (III-12)

Top3Vo _(i)=Average Vo _(i) of the lowest three collected Vo_(i)  (III-13)

TopHalfVo _(i)=Average Vo _(i) of lowest 50% of the collected Vo_(i)  (III-14)

AverageVo _(i)=Average Vo _(i) of all collected Vo _(i)  (III-15)

Individual Output Variability Gaps

GapVo _(i) =Vo _(i)−Selected Individual Metric from (III-9 toIII-15).  (III-16)

-   -   The preferred embodiment of GapVo_(i) is to use the Q1Vo_(i),        and to use the others to create intermediate gap closure goals.

Individual Output Metrics—Variability Ratio Vr_(i).

Q1Vr _(i)=Average Vr _(i) of lowest 25% of collected Vr _(i)  (III-17)

Q2Vr _(i)=Average Vr _(i) of 2^(nd) lowest 25% of the collected Vr_(i)  (III-18)

Q3Vr _(i)=Average Vr _(i) of 2^(nd) highest 25% of the collected Vr_(i)  (III-19)

Q4Vr _(i)=Average Vr _(i) of the highest 25% of the collected Vr_(i)  (III-20)

Top3Vr _(i)=Average Vr _(i) of the lowest three collected Vr_(i)  (III-21)

TopHalfVr _(i)=Average Vr _(i) of lowest 50% of the collected Vr_(i)  (III-22)

AverageVr _(i)=Average Vr _(i) of all collected Vr _(i)  (III-23)

Individual Variability Ratio Gaps

GapVr _(i) =Vr _(i)−Selected Individual Metric from (III-17 toIII-23).  (III-24)

-   -   The preferred embodiment of GapVr_(i) is to use the Q1Vr_(i),        and to use the others to create intermediate gap closure goals.

Individual Output Metrics—Variability Reduction Ratio Vrr_(i)

Q1Vrr _(i)=Average Vrr _(i) of lowest 25% of collected Vrr_(i)  (III-25)

Q2Vrr _(i)=Average Vrr _(i) of 2^(nd) lowest 25% of the collected Vrr_(i)  (III-26)

Q3Vrr _(i)=Average Vrr _(i) of 2^(nd) highest 25% of the collected Vrr_(i)  (III-27)

Q4Vrr _(i)=Average Vrr _(i) of the highest 25% of the collected Vrr_(i)  (III-28)

Top3Vrr _(i)=Average Vrr _(i) of the lowest three collected Vrr_(i)  (III-29)

TopHalfVrr _(i)=Average Vrr _(i) of lowest 50% of the collected Vrr_(i)  (III-30)

AverageVrr _(i)=Average Vrr _(i) of all collected Vrr _(i)  (III-31)

Individual Variability Reduction Ratio Gaps

GapVrr _(i)=Vrr_(i)−Selected Individual Metric from (III-25 toIII-31).  (III-32)

-   -   The preferred embodiment of GapVrr_(i) is to use the Q1Vrr_(i),        and to use the others to create intermediate gap closure goals.

Overall Unit Performance Metrics—Induced Variability

Q1Vi=Average Vi of lowest 25% of collected Vi  (III-33)

Q2 Vi=Average Vi of 2^(nd) lowest 25% of the collected Vi  (III-34)

Q3 Vi=Average Vi of 2^(nd) highest 25% of the collected Vi  (III-35)

Q4 Vi=Average Vi of the highest 25% of the collected Vi  (III-36)

Top3Vi=Average Vi of the lowest three collected Vi  (III-37)

TopHalfVi=Average Vi of lowest 50% of the collected Vi  (III-38)

AverageVi=Average Vi of all collected Vi  (III-39)

Overall Induced Variability Gaps

GapVi=Vi−Selected Individual Metric from (III-33 to III-39).  (III-40)

-   -   The preferred embodiment of GapVi is to use the Q1Vi, and to use        the others to create intermediate gap closure goals.

Overall Unit Performance Metrics—Output Variability

Q1Vo=Average Vo of lowest 25% of collected Vo  (III-41)

Q2Vo=Average Vo of 2^(nd) lowest 25% of the collected Vo  (III-42)

Q3Vo=Average Vo of 2^(nd) highest 25% of the collected Vo  (III-43)

Q4Vo=Average Vo of the highest 25% of the collected Vo  (III-44)

Top3Vo=Average Vo of the lowest three collected Vo  (III-45)

TopHalfVo=Average Vo of lowest 50% of the collected Vo  (III-46)

AverageVo=Average Vo of all collected Vo  (III-47)

Overall Output Variability Gaps

GapVo=Vo−Selected Individual Metric from (III-41 to III-47).  (III-48)

-   -   The preferred embodiment of GapVo is to use the Q1Vo, and to use        the others to create intermediate gap closure goals.

Overall Unit Performance Metrics—Variability Ratio

Q1Vr=Average Vr of lowest 25% of collected Vr  (III-49)

Q2Vr=Average Vr of 2^(nd) lowest 25% of the collected Vr  (III-50)

Q3Vr=Average Vr of 2^(nd) highest 25% of the collected Vr  (III-51)

Q4Vr=Average Vr of the highest 25% of the collected Vr  (III-52)

Top3Vr=Average Vr of the lowest three collected Vr  (III-53)

TopHalfVr=Average Vr of lowest 50% of the collected Vr  (III-54)

AverageVr=Average Vr of all collected Vr  (III-55)

Overall Variability Ratio Gaps

GapVr=Vr−Selected IndiVrdual Metric from (III-49 to III-55).  (III-56)

-   -   The preferred embodiment of GapVr is to use the Q1Vr, and to use        the others to create intermediate gap closure goals.

Overall Unit Performance Metrics—Variability Reduction Ratio Vrr

Q1Vrr=Average Vrr of lowest 25% of collected Vrr  (III-57)

Q2Vrr=Average Vrr of 2^(nd) lowest 25% of the collected Vrr  (III-58)

Q3Vrr=Average Vrr of 2^(nd) highest 25% of the collected Vrr  (III-58)

Q4Vrr=Average Vrr of the highest 25% of the collected Vrr  (III-59)

Top3Vrr=Average Vrr of the lowest three collected Vrr  (III-60)

TopHalfVrr=Average Vrr of lowest 50% of the collected Vrr  (III-61)

AverageVrr=Average Vrr of all collected Vrr  (III-62)

Overall Variability Reduction Ratio Gaps

GapVrr=Vrr−Selected Individual Metric from (III-57 to III-62).  (III-63)

-   -   The preferred embodiment of GapVrr is to use the Q1Vrr, and to        use the others to create intermediate gap closure goals.

In addition to the standard deviations Quartiles 1 given in the aboveparagraph, some process parameter average values can be similarlydivided into quartiles and reported back to participants. This is notthe preferred practice as the average values represent the set pointsetting and are considered proprietary by study participants. Oneexception to this is the column pressure of atmospheric crude units andvacuum units. These parameters averages can be reported back as higherpressure causing the distillation to be more difficult and less energyefficient. In reporting back the pressures, it is important to dividethe industry data into process types. In particular for vacuum unitthere are two main types (wet and dry vacuum units). The pressures canonly be compared with like types of vacuum units.

Column Pressure (P) Metrics

Q1P=Average P of lowest 25% of collected P  (III-64)

Q2P=Average P of 2^(nd) lowest 25% of the collected P  (III-65)

Q3P=Average P of 2^(nd) highest 25% of the collected P  (III-66)

Q4P=Average P of the highest 25% of the collected P  (III-67)

Top3P=Average P of the lowest three collected P  (III-68)

TopHalfP=Average P of lowest 50% of the collected P  (III-69)

AverageP=Average P of all collected P  (III-70)

Overall Variability Reduction Ratio Gaps

GapP=P−Selected Individual Metric from (III-64 to III-70).  (II-71)

-   -   The preferred embodiment of GapP is to use the Q1P, and to use        the others to create intermediate gap closure goals.

It is anticipated that individual unit types will contain certainvariables that the industry will find valuable to compare as averages,which will not be considered proprietary process information. Columnpressure is just an example, and other average values maybe selected forother processes. In some industries, the set points might not beconsidered proprietary, and industry participants might be willing toshare this information for comparative purposes. In such cases, thecritical set points might be collected and shared by this sametechnique.

Calculation of Economic Values of Closing Performance Gaps

Some of the key areas where a gap economic value from equations III-8,16, 24, 40, 48, 56, 63, and 71 can be estimated are:

Yield improvementsEnergy improvementsCapacity improvements.

These improvements can be achieved in at least three ways that can becalculated from the novel metrics of this invention:

The improvement achieved by matching the control performance benchmarkVrr and/or Vrr_(i) while making no changes in the induced variability Vior Vi_(i).The improvement achieved by matching the induced variability benchmarkVi or Vi_(i) while making no changes in the control assets performanceVrr or Vrr_(i).The improvement achieved by matching both the induced variabilitybenchmark Vi or Vi_(i) and the control assets performance Vrr or Vrr_(i)simultaneously.

FIG. 5 illustrates how variability reduction can affect production yieldand throughput. Time series A in FIG. 5 represents the present productvariation, which varies between the demonstrated upper and lower datalimits (bar 1), as dictated by the standard deviation of the data, Vo,which is calculated from the collected process data.

Time series B represents the time series benchmark generated fromcalculation of the benchmark achievable variation Vob, and the selectedbenchmark Variability Reduction Ratio benchmark Vrrb as given in theequation below:

Vob=Max[Vi*Min(Vrrb,Vrr),MinVo]  (IV-1)

-   -   Where Vob=benchmark achievable standard deviation        -   of the product measurement.    -   Vi=the induced variability of the unit being analyzed        -   Vrrb=the selected Vrr benchmark from equations III-57            -   through III-62 above        -   Vrr=the variability reduction ratio for the unit being            analyzed. Use of the actual value assures that if by chance            the Vrr of the process is better than the benchmark, that            the current performance will be used in the calculation.        -   MinVo=Minimum demonstrated Vo in the population, use of the            minimum in the collected industrial data assures that the            numbers calculated are limited to performance that has been            demonstrated to be achievable in actual industrial            application. Note that the average to the three best Vo's            can be used instead of the single best Vo to prevent            revealing any participants actual value.            Different values may be substituted into Equation IV-1            depending on the objective of the improvement analysis:

Case 1: Analysis of the Controls Improvement

Vi=the Vi of the unit

Vrr=a selected benchmark value from equations III-57 through III-62above.

Case 2: Analysis of Induced Variability Improvement

Vi=a selected benchmark value from equations III-33 through III-39above.

Vrr=the Vrr of the unit.

Case 3: Analysis of Simultaneous Induced Variability and ControlsImprovement

Vi=a selected benchmark value from equations III-33 through III-39above.

Vrr=a selected benchmark value from equations III-57 through III-62above.

One embodiment uses the overall unit Q1Vi, and Q1Vrr to simplifyanalysis, but note than a large combination of analysis are possible bysubstituting any combination of individual metrics and overall metricsfrom equations III-1 through III-63 above or values in the analysisequations. This preferred embodiment calculates the potential variationachievable if the unit process control assets performance can match thatof the 1^(st) quartile average, and limits the potential variation to beno smaller than the smallest demonstrated variations reported by theindustry data collection.

In an alternate embodiment, Vob is not calculated from Vrr and Vi asgiven in Equation IV-1, but instead Vob equal=is set to the average ofthe 1^(st) quartile Vo (Q1Vo from Equation III-41 above). However, thismethod may not be preferred, since it ignores the input variability thatthe unit faces. It might not be demonstrated by the industry that Q1Vocould be achieved stating with the level of induced variability the unitfaces.

Referring back to FIG. 5, now that we have explained how time series Bis established, we can now easily see the gap in performance. The Gap isVo-Vob. If the unit being analyzed can match the benchmark Vob, then thetime series of the process would be the same as that in time series B.

By application of the method described above we have established thattime series B has been demonstrated achievable in industry. Once timeseries B has been achieved, the opportunity exists to move the processset point to the existing process constraints to take advantage of thelower variation and achieve an economic benefit. This is done byadjusting the set point to push time series B against the mosteconomical constraint upper or lower bound depending on the bettereconomics. Three types of constraints are illustrated in FIG. 5:

The demonstrated data upper and lower limit—(bar 1).Or the product upper or lower specification constraint—(bar 2).A known process constraint upper or lower constraint that is a hardphysical constraint or a calculated constraint. Calculated constraintscan include limits inferred from other outputs or inputs includingcombination constraints. This constraint could be directly measured orcalculated by any conceivable method—(bar 3).

All of these constraint types can be used, however one preferredembodiment is the use of the “Same Limit Rule,” which means that theupper and lower bounds demonstrated in the collected data for the unitare used. This is the same as the Demonstrated Data Limit in FIG. 5 (Bar1).

The “Same Limit Rule” is preferred because its use will ensure that theeconomic value will be conservatively estimated, and the process isknown to be able to achieve these limits because the historical datacollected itself proves that to be so. This limit is illustrative andexemplary only, since any measured or calculated limit established byany method may be used.

In the refining industry, for example, the upper and lower productspecifications are not likely to be achievable because the overall plantoptimization LP model would have set the set points that the processruns under and the act of adjusting to the wider specification limitswould defeat the overall plant optimization. Adjusting to the knownprocess constraints is perfectly valid but requires the work toestablish the actual known limits, which is not a trivial task. Onemethod would be to communicate to the LP model the new capabilitydemonstrated by time series B, and a new soft limit would be calculatedby the LP. This would result in new bar 3 limits.

Referring back to FIG. 5, the process can now be improved by moving theset point such that the reduced variability is up against the processconstraint selected for analysis, that being bar 1, bar 2, or bar 3.

If the time series represents an output quality measure such as 90%point for a product of a crude distillation unit then this shift has aknown economic value at the plant and also infers a change in the volumeof the product produced. If the distribution is moved upwards, then thetemperature is increasing and the amount of volume of increasedproduction can be calculated from the boiling point curve for that crudefeed as given in FIG. 4.

If the time series represents a production rate, then the productionrate can be increased by moving upwards to the selected constraint. Inboth cases, the economic value of the increased production can becalculated.

Economic Value=increased volume*price of product  (IV-2)

In the case of a distillation unit, unless the overall throughput isincreased, then the improvement represents a yield improvement to a morevaluable product. Referring to FIG. 6, reduced variability results in anincrease in production of a more valuable product over a less valuableproduct.

Economic Value=Increase in draw 1*(price draw 1−price draw 2)  (IV-3)

An energy savings can be calculated directly from a reduction in thetemperature variations of the individual column distillation productstreams. FIG. 7 illustrates the basic concepts. All upward swings in thevariation of the temperature of the column products are assumed torequire the addition of heat into the unit. In the illustrated case, theheat source is a fired furnace with efficiency ε.

Energy Savings Value=PΣ ₁([0.5m ₁ Cp ₁(6σT ₁₁−6σT ₂₁)]/ε)  (IV-4)

Where P=price of energy in economic unit per unit of mass

-   -   1=streamidentifier. 1 can be just the side streams from        -   a distillation unit, or can encompass all exit streams        -   from the unit, or any subset being analyzed.            -   Σ₁=summation over all selected streams 1 to i    -   ε=unit heat source efficiency factor. In refining this is the        efficiency of        -   the unit fired heater. However the general form of equation            IV-4        -   allows C to represent the efficiency of any unit heat            source.    -   m₁=mass flow or stream 1.    -   Cp₁=heat capacity of stream 1    -   σT₁₁=standard deviation of temperature of column product stream        1 as        -   measured in the observation data.    -   σT₂₁=standard deviation of temperature benchmark selected from        -   the individual output variation benchmark equations III-9            through III-15 above.            Equation IV-4 is the preferred embodiment. However,            depending on the refinery control philosophy (30 or 20            control limits) the constant 6 (corresponding to 30) in            equation IV-4 can be replaced with a constant value of 4            (corresponding to 20).

The aforementioned methods of calculating quality, yield and energyimprovement are illustrative and exemplary, since quality, yield, andenergy improvement may be determined using other measurements andcalculations.

Graphical Construct for Visualizing and Diagnosing Overall UnitPerformance.

FIG. 8 shows a novel graphical construct according to one embodiment todisplay the overall performance of a unit by using the overall metricsVo, Vi, and Vrr calculated by equations III-40, III-48, and III-63respectively. This graphical construct will now be referred to as a“Variability Graph.”

The Variability Graph is constructed for one unit type at a time. Allunits of the same type under analysis can be plotted on the same graphto indicate their relative performance. The example unit type selectedfor one embodiment is a crude distillation unit, however, similargraphic constructs can be developed for all unit types.

The X-axis of this graph is the induced variability metric, Vi, which iscalculated by equation III-40. For crude units, Vi is given as thestandard deviation of the side draw temperatures of the crude unit sidestreams in degrees F. The side stream draw temperature is a measurementof the composition of the stream, and the variation of the temperatureis a measurement of the quality of the material. The induced variabilityrepresents the amount of side stream temperature variation that theinput variation would cause the side stream products to have if notremoved by the unit controls.

The Y-axis of this graph is the output variability metric Vo, which iscalculated by equation III-48. For crude units, Vo is given as theactual standard deviation of the side draw temperatures in degrees F. ascalculated from the raw observation data. Thus Vo is the key actualcolumn control performance.

Each unit in the study can be plotted using the units Vo and Vi datapoints. Point 1, 2, and 3 in FIG. 8 represent the overall performance ofthree crude units. Since the most desirable condition is zero inducedvariability and zero product variability, the most desirable spot on thegraph is at the origin.

The vertical dashed lines on FIG. 8 divide the X-axis axis (Vi inducedvariability) into four regions representing the four quartiles of Viperformance. Quartile 1 is the lowest variability and the most desirablequartile to be in. The horizontal lines on the FIG. 8 divide the Y-axis(Vo output variability) into four regions representing the fourquartiles of Vo performance. Quartile 1 is the lowest variability andthe most desirable quartile.

The radial diagonal lines that extend outward from the origin divide thegraph space into four regions representing the four quartiles ofvariability reduction performance as measured by Vrr which is calculatedby equation III-63. Quartile 1 is the lowest variability and the mostdesirable quartile.

To understand why the radial lines represent the Vrr, consider point 4on FIG. 8, the angle α and the right triangle formed by the three pointsof the origin, point 4 and intercept of the x-axis of a line droppedstraight down from point 4. The tangent of α is 3/10, which is Vo/Vi.Looking at equation 1-6 and 1-7, it can be seen that Vr=Vo/Vi andVrr=1−Vo/Vi. Thus, the radial lines directly represent Vr and Vrr, andalso represent lines of constant controller performance over any valueof induced variability recorded in the industrial data collected.

With the information conveyed by the Variability Graph, one skilled inthe art can ascertain knowledge about a unit's performance by simpleexamination of the region of the graph where the point representing theunit's performance falls.

For example, consider Point 1 in FIG. 8. This unit is operating verywell. The unit's overall performance is measured by Vo and Vo is in the1^(st) quartile. The induced variability is measured by Vi, and Vi isalso in the 1^(st) quartile. The performance of the controls is measuredby the Vr and the Vrr is also in the first quartile. Point 1 is one ofthe very best performing units in the entire study. In fact it is in thetop 0.25*0.25*0.25=1/64 of the study population.

Now we will look at Point 2 in FIG. 8. Point two is a poor overallperforming unit because the main measurement of success is Vo, and theVo of the unit is in the 4^(th) quartile. Looking at the Vi we can seethat the induced variability of the unit is extremely high in comparisonto the unit population and is in the high end of the 4^(th) quartile.

Previously, management might erroneously conclude that this unitrepresented by point 2 in FIG. 8 is in need of better process controls.An investment in expensive new control applications for the unit mightbe pursued. However, examination of the Vrr shows that this unit alreadyhas exceptional control performance. The Vrr is quartile 1. In fact, ifextending a radial line from point 2 to the origin as is shown in FIG.8, it passes through point 1 which represents one of the very bestperforming units in the industrial data. Therefore, it can be concludedthat this is not a unit controls problem. This is a problem caused byexcessively high induced variability in the feeds to the unit. Even thevery best controller in the study could not achieve outstanding Voresults with this high an induced variability. The diagnosis then is tosearch out the causes of the high induced variability. This can be doneby looking at the quartile ratings of the three inducing parameters(feed rate, feed temperature and feed API) from examination of theresults of equation III-8.

As we improve the induced variability of the unit presented by point 2,we will be improving Vi with constant controller performance. Thus theunit performance should improve and travel down a line of constant Vrrapproximated by Line b. As can be seen on the graph at point 5, if theinduced variability can just be reduced to 3^(rd) quartile which isstill higher than the study average that the overall unit performance asmeasured by Vo will be 1^(st) quartile.

Now we will examine point 3 in FIG. 8. The unit represented by point 3is also an overall poor performer as measured by Vo which is 4^(th)quartile. However, examination of the induced variability shows that theunit has no excuses since the induced variability is low and in the1^(st) quartile. The problem with this unit is the poor performance ofthe unit controls as witnessed by the poor 4^(th) quartile Vrr. Thisunit is in need of tuning the existing controls, and potentially newcontrol applications.

It should be noted that it has been demonstrated in industrialapplications that points in the region of the graph occupied by point 3can also have mechanical problems that prevent the unit from performingwell that are independent of the controls themselves. The unit shouldalso be checked for mechanical integrity of the column internals. If theunit is mechanically sound, then the existing controls might be poorlytuned. Units in this region of the graph often have controls that arecausing more harm than good. Simply placing the offending controls inopen loop might reduce output variability dramatically.

Assuming that the unit is mechanically sound, as we work to improvecontroller performance, the unit performance will improve and traveldown a line of constant induced variability approximated by Line c. Ascan be seen, the unit will achieve 1^(st) quartile overall performanceif the controls performance measured by Vrr can just achieve 3^(rd)quartile as shown by Point 6 in FIG. 8.

As previously stated, Variability Graphs have been created through thisinvention for all refining unit types. Some units have multiple graphs.For example, Fluid Catalytic Cracking (FCC) units typically have 5graphs. The FCC unit can be placed on one graph showing the finalproducts from the main fractionator. However, there is more informationto be displayed for a FCC unit. The reaction section of the unit must beanalyzed separately for flue gas oxygen or carbon monoxide controldepending on the unit combustion mode (complete or incompletecombustion). In addition, the unit wet gas compressor or air blowercontrols must be analyzed separately depending on which limits unitthroughput. This results in 5 Variability Graphs in the FCC analysis.This further illustrates the general use of variability graphs toanalyze subparts of the process.

Additional Variability Graphs can be constructed on a stream by streambasis or for specialized portions of the unit operation. The use of thevariability graphs for explaining stream-by-stream performance isillustrative and exemplary, since the graphs may be used to analyze anycontrol system.

On-Line Real Time Analysis with the Metrics

It should be recognized that all calculations within this patentapplication can be automated and placed in real time monitoring andcontrol applications to deliver process alarms, invoke expert systems orlogic trees, provide feedback to control loops, and directly deliver setpoints.

Automated Delivery of Advice by Quartile

The division of the key metrics Vo, Vi, and Vrr developed above allowthe automated delivery of advice on the performance of the unit. Acombined “Performance Key” metric, Vo-Vi-Vrr, is developed by theconcatenation of the three measures separated by dashes. For example, ifVo is quartile 3, and Vi is quartile 1 and Vrr is quartile 4, then thePerformance Key metric Vo-Vi-Vrr would be 3-1-4. Since each measure has4 quartiles, there are 4*4*4=64 potential values of Vo-Vi-Vrr. For eachunit type a table can be built that delivers advice based on combinedmetric. Note that any combination of the metrics Vo and Vi can be used,as values of just Vo and Vi contain within them the value of Vrr. Theaddition of Vr or Vrr allows the space to be further divided into 64regions for diagnosis.

A computer program matches the combined metric to one of the 64 optionsdefined by the Performance Key and delivers advice appropriate for theunit performance. An example of this advice for a vacuum unit is givenin Table 500. Table 500 is illustrative and exemplary, and a number ofsimilar tables can be used for different types of units. The advice intable 500 is exemplary only, and additional or alternate advicestatements can be automatically constructed. For example, the main inputvariables variability can be automatically compared to their quartilesto relate which of the inputs is most responsible for high inducedvariability.

As an example of the use of the automated advice from table 500, aPerformance Key of 3-1-4 using the automated advice from Table 500 woulddeliver the following:

Advice 1 The overall Performance Key=3-1-4.

Advice 2 Overall unit performance is below study average.

Advice 3 Poor variability reduction with controls.

Advice 4 Excellent low input variability.

Advice 5 Tune existing controls and consider control applicationimprovements.

It should be noted that additional and more detailed automatedinterpretation and advice could be delivered by more detailed automatedanalysis of any of the metrics of this invention. All are contemplatedand within the scope of this invention.

Vr Vector Representation

An alternate method of analysis of a unit's performance based on the Voand Vi is the Vr Vector Representation.

|Vr|=(Vo ² +Vi ²)^(0.5)  (V-1)

α=Tan⁻¹ Vr  (V-2)

Where |Vr|=The magnitude of the Vr vector=the hypotenuse of the righttriangle formed with Vo and Vi and the sides.

α=the Vr angle

The Vr vector represents the total variability experienced by the unitin analysis. The larger the value |Vr| the more “shook up” the unit is.It is desirable to have lower values of |Vr|. The angle α represents theamount of variability that has been reduced by the units controls. Thesmaller the value of α, the more variability has been reduced. |Vr| andα can be placed into quartiles and placed into a graph similar to FIG. 8as shown in FIG. 10. Advice similar to that in Table 500 can also bedeveloped and delivered using |Vr| and α.

The Vr vector presents the entire performance picture in one vector. Itis mathematically useful to interpret Vr in polar coordinates, for thepurpose of creating generalized quartiles that replace the threequartile sets previously described with one set of quartiles.

The Vr vector interpretation provides a basis for analyzing theinformation contained in two vectors, such as would occur when comparingthe variability performance of two similar units or the same unit at twodifferent times (as in an on-line application). Vector algebra can beused in these cases, namely, vector addition, subtraction, and dot andcross products.

As shown in FIG. 9, one embodiment of a system used to perform themethod includes a computing system. The hardware consists of a processor910 that contains adequate system memory 920 to perform the requirednumerical computations. The processor 910 executes a computer programresiding in system memory 920 to perform the method. Video and storagecontrollers 930 are required to enable the operation of display 940. Thesystem includes various data storage devices for data input includingfloppy disk units 950, internal/external disk drives 960, internalCD/DVDs 970, tape units 980, and other types of electronic storage media990. The aforementioned data storage devices are illustrative andexemplary only. These storage media are used to enter and store theprocess data frequency and loss data to the system, store thecalculations, and store the system-produced analysis reports and graphs.The calculations can apply statistical software packages or can beperformed from the data entered in spreadsheet formats using MicrosoftExcel, for example. The analysis calculations are performed using eithercustomized software programs designed for company-specific systemimplementations or by using commercially available software that iscompatible with Excel or other database and spreadsheet programs. Thesystem can also interface with proprietary or public external storagemedia 1030 to link with other databases to provide additional data to beapplied to the performance measurement benchmarking system and methodcalculations. The output devices can be a telecommunication device 1000to transmit the calculation worksheets and other system produced graphsand reports via an intranet or the Internet to management or otherpersonnel, printers 1010, electronic storage media similar to thosementioned as input devices and proprietary storage databases 1030. Theseoutput devices are illustrative and exemplary only. If the analysis isto be performed on-line for real-time process monitoring and control,then the above system can also have additional sources of input andoutput.

The manufacturing control system 2000, which can include programmablelogic controllers, distributed control systems, or field bus devices,would provide live data to the processors 910. It is also possible forthe manufacturing control system 2000, which contains central processingsystems, to take on all or part of the tasks of the processor 910. Theresults of the methods and calculations can be received from theprocessors 910 for use in real time control and alarming inside themanufacturing control system 2000.

Additional data for the method may come from the process data historian2010, which keeps records of process variable and parameter values withtime stamps and can also share any portion of the calculations performedby the processors 910. The results of the calculations from theprocessors 910 can also be stored in the process data historian 2010.

Input data can also be received by the processors 910 from externalprocess control systems 2020 that reside on computers external to themanufacturing control system 2000. The results of the methods andcalculations can be received from the processors 910 for use in realtime control and alarming inside the external process control systems2020.

The manufacturing information system 2030 can receive data and resultsfrom the processors 910 either directly or secondarily from themanufacturing control system 2000 the process data historian 2010 or theexternal process control systems 2020. This data can be used to createkey performance indicators such as Vi, Vo, and Vrr for plots and writtenreports. Information from the manufacturing information system 2030 canbe passed on to the company information systems 2040 the companyintranet or world wide web 2050 for use in any conceivable purpose.

The foregoing disclosure and description of the preferred embodiments ofthe invention are illustrative and explanatory thereof, and variouschanges in the details of the illustrated system and method may be madewithout departing from the scope of the invention. In particular, thesystem can operate as a stand alone analysis method without the processdata historian 2010, external process control systems 2020,manufacturing information system 2030, company information systems 2040,and company intranet or world wide web 2050. Additionally, an embodimentof the system can be on-line live by incorporating the processor 910functions into the manufacturing control system 2000, the process datahistorian 2010, the external process control systems 2020, or themanufacturing information system 2030.

TABLE 100 Input Data Collected by Unit Type Unit Type Input ParametersCrude Units Crude Flow Crude Temp Crude API_((Inferred)) Furnace OutletTemp Column Pressure Vacuum Units ATB Flow ATB Temp ATB API_((Inferred))Furnace Outlet Temp Column Pressure FCC Units Fresh Feed Rate PreheatTemp Riser Outlet Temp O₂ or CO Vol % Air or WG Flow Hydrocrackers FreshFeed Rate WABT Recycle Flow Reformers Fresh Feed Rate WAITLHSV_((Inferred)) Hydrotreaters Fresh Feed WABT LHSV_((Inferred)) CokersFresh Feed Furnace Outlet Temp Recycle Flow CFR

TABLE 200 Output Data Collected by Unit Type Unit Type Output ParametersCrude Units Draw Temp of each Draw Flow Rate of each Bottoms Flow RateBottoms Flow Temp side draw product side draw product Vacuum Units DrawTemp of each Draw Flow Rate of each Bottoms Flow Rate Bottoms Flow Tempside draw product side draw product FCC Units Draw Temp of each DrawFlow Rate of each Bottoms Flow Rate Bottoms Flow Temp side draw productside draw product Hydrocrackers Draw Temp of each Draw Flow Rate of eachBottoms Flow Rate Bottoms Flow Temp side draw product side draw productReformers RONC Octane Product Flow Rate Product Flow Temp AnalysisHydrotreaters Draw Temp of each Draw Flow Rate of each Bottoms Flow RateBottoms Flow Temp side draw product side draw product Cokers Draw Tempof each Draw Flow Rate of each Bottoms Flow Rate Bottoms Flow Temp sidedraw product side draw product

TABLE 300 Output Data Collected by Unit Type Crude-Switch Unit TypeNormal-State Data or Drum-Switch Data Crude Units 24-hr datasets - 3 ea24-hr datasets - 3 ea Vacuum Units 24-hr datasets - 3 ea 24-hrdatasets - 3 ea Reformers 12-hr datasets - 3 ea FCC Units 12-hrdatasets - 3 ea Hydrocrackers 12-hr datasets - 3 ea Hydrotreators 12-hrdatasets - 3 ea Cokers 12-hr datasets - 3 ea 12-hr datasets - 3 ea

TABLE 400 Example Vi Gain Matrix for a Crude Unit Crude Feed RateFurnace Inlet Temp API, Side-Draw Stream ° F./vol % ° F./° F. ° F./APILSR 16.9 1.0 13.8 Med Naphtha 12.9 1.0 12.4 Hvy Naphtha 12.2 1.0 11.1 LtKerosene 11.3 1.0 9.7 Kerosene 11.2 1.0 8.4 Diesel 11.4 1.0 7.1 AGO 11.51.0 5.7 HGO 11.7 1.0 4.4 LVGO 12.8 1.0 3.0

TABLE 500 Automated Advice using the “Performance Key” derived fromVo-Vi-Vrr. Performance Key Advice 2 Advice 3 1-1-1 Excellent overallperformance. Excellent variability reduction with controls. 1-2-1Excellent overall performance. Excellent variability reduction withcontrols. 1-3-1 Excellent overall performance. Excellent variabilityreduction with controls. 1-4-1 Excellent overall performance. Excellentvariability reduction with controls. 1-1-2 Excellent overallperformance. Better than average variability reduction with controls.1-2-2 Excellent overall performance. Better than average variabilityreduction with controls. 1-3-2 Excellent overall performance. Betterthan average variability reduction with controls. 1-4-2 Excellentoverall performance. Better than average variability reduction withcontrols. 1-1-3 Excellent overall performance. Below average variabilityreduction with controls. 1-2-3 Excellent overall performance. Belowaverage variability reduction with controls. 1-3-3 Excellent overallperformance. Below average variability reduction with controls. 1-4-3Excellent overall performance. Below average variability reduction withcontrols. 1-1-4 Excellent overall performance. Poor variabilityreduction with controls. 1-2-4 Excellent overall performance. Poorvariability reduction with controls. 1-3-4 Excellent overallperformance. Poor variability reduction with controls. 1-4-4 Excellentoverall performance. Poor variability reduction with controls. 2-1-1Better than study average overall performance. Excellent variabilityreduction with controls. 2-2-1 Better than study average overallperformance. Excellent variability reduction with controls. 2-3-1 Betterthan study average overall performance. Excellent variability reductionwith controls. 2-4-1 Better than study average overall performance.Excellent variability reduction with controls. 2-1-2 Better than studyaverage overall performance. Better than average variability reductionwith controls. 2-2-2 Better than study average overall performance.Better than average variability reduction with controls. 2-3-2 Betterthan study average overall performance. Better than average variabilityreduction with controls. 2-4-2 Better than study average overallperformance. Better than average variability reduction with controls.2-1-3 Better than study average overall performance. Below averagevariability reduction with controls. 2-2-3 Better than study averageoverall performance. Below average variability reduction with controls.2-3-3 Better than study average overall performance. Below averagevariability reduction with controls. 2-4-3 Better than study averageoverall performance. Below average variability reduction with controls.2-1-4 Better than study average overall performance. Poor variabilityreduction with controls. 2-2-4 Better than study average overallperformance. Poor variability reduction with controls. 2-3-4 Better thanstudy average overall performance. Poor variability reduction withcontrols. 2-4-4 Better than study average overall performance. Poorvariability reduction with controls. 3-1-1 Overall unit performance isbelow study average. Excellent variability reduction with controls.3-2-1 Overall unit performance is below study average. Excellentvariability reduction with controls. 3-3-1 Overall unit performance isbelow study average. Excellent variability reduction with controls.3-4-1 Overall unit performance is below study average. Excellentvariability reduction with controls. 3-1-2 Overall unit performance isbelow study average. Better than average variability reduction withcontrols. 3-2-2 Overall unit performance is below study average. Betterthan average variability reduction with controls. 3-3-2 Overall unitperformance is below study average. Better than average variabilityreduction with controls. 3-4-2 Overall unit performance is below studyaverage. Better than average variability reduction with controls. 3-1-3Overall unit performance is below study average. Below averagevariability reduction with controls. 3-2-3 Overall unit performance isbelow study average. Below average variability reduction with controls.3-3-3 Overall unit performance is below study average. Below averagevariability reduction with controls. 3-4-3 Overall unit performance isbelow study average. Below average variability reduction with controls.3-1-4 Overall unit performance is below study average. Poor variabilityreduction with controls. 3-2-4 Overall unit performance is below studyaverage. Poor variability reduction with controls. 3-3-4 Overall unitperformance is below study average. Poor variability reduction withcontrols. 3-4-4 Overall unit performance is below study average. Poorvariability reduction with controls. 4-1-1 Overall unit performance is4th quartile. Excellent variability reduction with controls. 4-2-1Overall unit performance is 4th quartile. Excellent variabilityreduction with controls. 4-3-1 Overall unit performance is 4th quartile.Excellent variability reduction with controls. 4-4-1 Overall unitperformance is 4th quartile. Excellent variability reduction withcontrols. 4-1-2 Overall unit performance is 4th quartile. Better thanaverage variability reduction with controls. 4-2-2 Overall unitperformance is 4th quartile. Better than average variability reductionwith controls. 4-3-2 Overall unit performance is 4th quartile. Betterthan average variability reduction with controls. 4-4-2 Overall unitperformance is 4th quartile. Better than average variability reductionwith controls. 4-1-3 Overall unit performance is 4th quartile. Belowaverage variability reduction with controls. 4-2-3 Overall unitperformance is 4th quartile. Below average variability reduction withcontrols. 4-3-3 Overall unit performance is 4th quartile. Below averagevariability reduction with controls. 4-4-3 Overall unit performance is4th quartile. Below average variability reduction with controls. 4-1-4Overall unit performance is 4th quartile. Poor variability reductionwith controls. 4-2-4 Overall unit performance is 4th quartile. Poorvariability reduction with controls. 4-3-4 Overall unit performance is4th quartile. Poor variability reduction with controls. 4-4-4 Overallunit performance is 4th quartile. Poor variability reduction withcontrols. Performance Key Advice 4 Advice 5 1-1-1 Excellent low inputvariability. This unit is a good candidate for RTO. 1-2-1 Good low inputvariability. This unit is a good candidate for RTO. 1-3-1 Higher inputvariability than the study average. Reduce input variability foradditional performance. 1-4-1 Excessively high input variability. Reduceinput variability for additional performance. 1-1-2 Excellent low inputvariability. This unit is a good candidate for RTO. 1-2-2 Good low inputvariability. This unit is a good candidate for RTO. 1-3-2 Higher inputvariability than the study average. Reduce input variability to improveperformance. 1-4-2 Excessively high input variability. Reduce inputvariability to improve performance. 1-1-3 Excellent low inputvariability. Tune existing controls for improved performance. 1-2-3 Goodlow input variability. Tune existing controls for improved performance.1-3-3 Higher input variability than the study average. Reduce inputvariability and tune existing controls. 1-4-3 Excessively high inputvariability. Reduce input variability for additional performance. 1-1-4Excellent low input variability. Tune existing controls for improvedperformance. 1-2-4 Good low input variability. Tune existing controlsfor improved performance. 1-3-4 Higher input variability than the studyaverage. Tune existing controls for improved performance. 1-4-4Excessively high input variability. Tune existing controls and reduceinput variability. 2-1-1 Excellent low input variability. This unit is agood candidate for RTO. 2-2-1 Good low input variability. This unit is agood candidate for RTO. 2-3-1 Higher input variability than the studyaverage. Reduce input variability to improve performance. 2-4-1Excessively high input variability. Reduce input variability to improveperformance. 2-1-2 Excellent low input variability. This unit is a goodcandidate for RTO. 2-2-2 Good low input variability. This unit is a goodcandidate for RTO. 2-3-2 Higher input variability than the studyaverage. Reduce input variability for additional performance. 2-4-2Excessively high input variability. Reduce input variability foradditional performance. 2-1-3 Excellent low input variability. Tuneexisting controls for improved performance. 2-2-3 Good low inputvariability. Tune existing controls for improved performance. 2-3-3Higher input variability than the study average. Reduce inputvariability and tune existing controls. 2-4-3 Excessively high inputvariability. Reduce input variability to improve performance. 2-1-4Excellent low input variability. Tune existing controls for improvedperformance. 2-2-4 Good low input variability. Tune existing controlsfor improved performance. 2-3-4 Higher input variability than the studyaverage. Tune existing controls for improved performance. 2-4-4Excessively high input variability. Tune existing controls and reduceinput variability. 3-1-1 Excellent low input variability. Factors notmeasured by this study are affecting performance. 3-2-1 Good low inputvariability. Factors not measured by this study are affectingperformance. 3-3-1 Higher input variability than the study average.Reduce input variability to improve performance. 3-4-1 Excessively highinput variability. Reduce input variability to improve performance.3-1-2 Excellent low input variability. Factors not measured by thisstudy are affecting performance. 3-2-2 Good low input variability.Factors not measured by this study are affecting performance. 3-3-2Higher input variability than the study average. Reduce inputvariability to improve performance. 3-4-2 Excessively high inputvariability. Reduce input variability to improve performance. 3-1-3Excellent low input variability. Tune existing controls then considerimproved control applications. 3-2-3 Good low input variability. Tuneexisting controls then consider improved control applications. 3-3-3Higher input variability than the study average. Reduce inputvariability, tune existing controls, consider control applications.3-4-3 Excessively high input variability. Reduce input variability, tuneexisting controls, consider control applications. 3-1-4 Excellent lowinput variability. Tune existing controls and consider controlapplication improvements. 3-2-4 Good low input variability. Tuneexisting controls and consider control application improvements. 3-3-4Higher input variability than the study average. Reduce inputvariability, tune existing controls, consider control applications.3-4-4 Excessively high input variability. Reduce input variability, tuneexisting controls, consider control applications. 4-1-1 Excellent lowinput variability. Factors not measured by this study are affectingperformance. 4-2-1 Good low input variability. Factors not measured bythis study are affecting performance. 4-3-1 Higher input variabilitythan the study average. Reduce input variability to improve performance.4-4-1 Excessively high input variability. Reduce input variability toimprove performance. 4-1-2 Excellent low input variability. Factors notmeasured by this study are affecting performance. 4-2-2 Good low inputvariability. Factors not measured by this study are affectingperformance. 4-3-2 Higher input variability than the study average.Reduce input variability for additional performance. 4-4-2 Excessivelyhigh input variability. Reduce input variability for additionalperformance. 4-1-3 Excellent low input variability. Tune existingcontrols then consider improved control applications. 4-2-3 Good lowinput variability. Tune existing controls then consider improved controlapplications. 4-3-3 Higher input variability than the study average.Reduce input variability, tune existing controls and consider controlapplications. 4-4-3 Excessively high input variability. Reduce inputvariability, tune existing controls and consider control applications .. . 4-1-4 Excellent low input variability. Tune existing controls andconsider control application improvements. 4-2-4 Good low inputvariability. Tune existing controls and consider control applicationimprovements. 4-3-4 Higher input variability than the study average.Reduce input variability, tune existing controls and consider controlapplications. 4-4-4 Excessively high input variability. Reduce inputvariability, tune existing controls and consider control applications.

We claim:
 1. A computer implemented method for estimating the economicvalue of closing performance gaps of an industry unit comprising thesteps of: determining, by a processor, output variability for the unit;determining, by a processor, overall induced variability for the unit;determining, by the processor, overall variability reduction ratio forthe unit; determining, by the processor, a benchmark achievable standarddeviation for the unit based on overall induced variability and overallvariability reduction ratio for the unit; determining a performance gapfor the unit based on the benchmark achievable standard deviation andthe output variability of the unit; and estimating the economic valuecorresponding to improving the performance gap for the unit.
 2. Thecomputer-implemented method of claim 1, wherein the economic value isestimated for increasing the unit's production yield.
 3. Thecomputer-implemented method of claim 1, wherein the economic value isestimated based on the increase in the unit's product yield and theprice of the product produced by the unit.
 4. The computer-implementedmethod of claim 1 wherein the economic value is estimated for improvingthe unit's energy savings.
 5. The computer-implemented method of claim1, wherein the economic value is estimated for improving the unit'scapacity.
 6. A system for estimating the economic value of closingperformance gaps of an industry unit comprising: a server, comprising: aprocessor, and a storage subsystem; a database stored by the storagesubsystem comprising: operational performance data for the unitcomprising input and output variable values; a computer program storedby the storage subsystem, when executed causing the processor to:determine output variability for the unit; determine overall inducedvariability for the unit; determine overall variability reduction ratiofor the unit; determine a benchmark achievable standard deviation forthe unit based on overall induced variability and overall variabilityreduction ratio for the unit; determine a performance gap for the unitbased on the benchmark achievable standard deviation and the outputvariability of the unit; and estimate the economic value correspondingto improving the performance gap for the unit.
 7. The system of claim 6,wherein the economic value is estimated for increasing the unit'sproduction yield.
 8. The system of claim 6, wherein the economic valueis estimated based on the increase in the unit's product yield and theprice of the product produced by the unit.
 9. The system of claim 6,wherein the economic value is estimated for improving the unit's energysavings.
 10. The system of claim 6, wherein the economic value isestimated for improving the unit's capacity.